TSTA: thread and SIMD-based trapezoidal pairwise/multiple sequence-alignment method

GigaByte. 2024 Nov 5:2024:gigabyte141. doi: 10.46471/gigabyte.141. eCollection 2024.

Abstract

The rapid advancements in sequencing length necessitate the adoption of increasingly efficient sequence alignment algorithms. The Needleman-Wunsch method introduces the foundational dynamic-programming matrix calculation for global alignment, which evaluates the overall alignment of sequences. However, this method is known to be highly time-consuming. The proposed TSTA algorithm leverages both vector-level and thread-level parallelism to accelerate pairwise and multiple sequence alignments.

Availability and implementation: Source codes are available at https://github.com/bxskdh/TSTA.

Grants and funding

This work was supported by the National Key Research and Development Program of China (2022YFC3400300 to JR) and the Innovation Program of the Chinese Academy of Agricultural Sciences.